Rating 4.73 out of 5 (948 ratings in Udemy)
What you'll learn- Apply already trained YOLO v3-v4 for Object Detection on image, video and in real time with camera
- Label own dataset and structure files in YOLO format
- Train YOLO v3-v4 detector in Darknet framework
- Assemble custom dataset in YOLO format
- Convert existing dataset of Traffic Signs in YOLO format
- Build individual PyQt graphical user interface for Object Detection based on YOLO v3-v4 algorithm
Description Rating 4.73 out of 5 (948 ratings in Udemy)
What you'll learn- Apply already trained YOLO v3-v4 for Object Detection on image, video and in real time with camera
- Label own dataset and structure files in YOLO format
- Train YOLO v3-v4 detector in Darknet framework
- Assemble custom dataset in YOLO format
- Convert existing dataset of Traffic Signs in YOLO format
- Build individual PyQt graphical user interface for Object Detection based on YOLO v3-v4 algorithm
DescriptionIn this hands-on course, you'll train your own Object Detector using YOLO v3-v4 algorithms.
As for beginning, you’ll implement already trained YOLO v3-v4 on COCO dataset. You’ll detect objects on image, video and in real time by OpenCV deep learning library. The code templates you can integrate later in your own future projects and use them for your own trained YOLO detectors.
After that, you’ll label individual dataset as well as create custom one by extracting needed images from huge existing dataset.
Next, you’ll convert Traffic Signs dataset into YOLO format. Code templates for converting you can modify and apply for other datasets in your future work.
When datasets are ready, you’ll train and test YOLO v3-v4 detectors in Darknet framework.
As for Bonus part, you’ll build graphical user interface for Object Detection by YOLO and by the help of PyQt. This project you can represent as your results to your supervisor or to make a presentation in front of classmates or even mention it in your resume.
Content Organization. Each Section of the course contains:
Video Lectures of the course have SMART objectives:
S - specific (the lecture has specific objectives)
M - measurable (results are reasonable and can be quantified)
A - attainable (the lecture has clear steps to achieve the objectives)
R - result-oriented (results can be obtained by the end of the lecture)
T - time-oriented (results can be obtained within the visible time frame)
